Facts and figures about UK taxes, benefits and public spending.
Income distribution, poverty and inequality.
Slides, video clips and interactive tools.
Analysing government fiscal forecasts and tax and spending.
 ESRC Centre for the Microeconomic Analysis of Public Policy. 
Analysis of the fiscal choices an independent Scotland would face.
Case studies that give a flavour of the areas where IFS research has an impact on society.
Reforming the tax system for the 21st century.
A peerreviewed quarterly journal publishing articles by academics and practitioners.


Centre for Microdata Methods and Practice (cemmap)

Date started: 19 August 2002
The Centre for Microdata Methods and Practice (c emmap) provides a focus for development, understanding and application of methods for modelling individual behaviour, the influences on it and the impact of policy interventions. The microdata studied at cemmap are the survey data resources recording behaviour and the surrounding social and economic environment. The microdata methods studied at cemmap are the statistical and econometric tools used to identify and estimate models of behaviour.  To stimulate development of microdata methods and practice.
 To stimulate the application of microdata methods to substantive social science issues.
 To offer courses on microdata methods and practice.
 To develop a network of associates and a research environment and infrastructure.
cemmap was launched at a conference on December 6th 2001 with speakers from the Civil Service and industry. The year 2000 Nobel Laureates in Economics, James Heckman and Daniel McFadden were the keynote speakers. The Centre is a joint venture by the Institute for Fiscal Studies and the Department of Economics at University College London. It is part of the Institute for Fiscal Studies and is directed by Andrew Chesher, Ian Crawford, Hidehiko Ichimura and Frank Windmeijer.
All available publications

In this paper, we propose a general method for testing inequality restrictions on nonparametric functions.


This paper introduces a bivariate version of the generalized accelerated failure time model. It allows for simultaneity in the econometric sense that the two realized outcomes depend structurally on each other.


This paper was presented at the Asian Meeting of Econometrics Society 2013 in Singapore on 2 to 4 August 2013.


This paper was presented at an Econometrics Seminar at Yale University in February 2013.


This paper asks which aspects of a structural Nonparametric Instrumental Variables Regression (NPIVR) can be identified well and which ones cannot.


In this note, we define a new property of functions called control function separability and show it provides a complete characterization of the structural systems of simultaneous equations in which the control function procedure is valid.


This paper was presented at Mathematisches Forschungsinstitut Oberwolfach conference on "Mathematical Statistics of Partially Identified Objects" on 22 April 2013.


This paper was presented at the 2013 Cowles Foundation for Research in Economics conference in Econometrics: Partial Identification, Weak Identification, and Related Econometric Problems. The conference took place on 5 June 2013 at YALE SOM, Watson Center.


This paper introduces average treatment effects conditional on the outcomes variable in an endogenous setup where outcome Y, treatment X and instrument Z are continuous.


This paper studies the identification of nonseparable models with continuous, endogenous regressors, also called treatments, using repeated cross sections.


We investigate the role of complementarities in production and skill mobility across cities.


This paper was presented at the cemmap conference "Recent Contributions to Inference in Game Theoretic Models" at UCL on 8 June 2013.


This article outlines how a home visiting intervention in Colombia, delivered at scale through partnering with existing social welfare systems, successfully increased the variety of play materials and play activities in poor households with children aged between 1 and 2 years at the start of the intervention.


Presentation to launch the Fiscal Studies Special Issue June 2013 on the Microeconomic Consequences of the Great Recession, given at IFS on 12 June 2013.


This paper analyses the career progression of skilled and unskilled workers, with a focus on how careers are affected by economic downturns and whether formal skills, acquired early on, can shield workers from the effect of recessions.


We extend the searchmatching model of the marriage market of Shimer and Smith (200) to allow for labour supply and home production.


In this paper we specify and use a new duration model to study joint retirement in married couples using the Health and Retirement Study.


We study nonparametric identification of singleagent discrete choice models for bundles and binary games of complete information.


In this paper we introduce a new approach to estimating a differentiated product demand system that allows for error in market shares as measures of choice probabilities.


This paper develops methodology for semiparametric panel data models in a setting where both the time series and the cross section are large.


This paper studies the problem of specification testing in partially indentified models defined by a finite number of moment equalities and inequalities (i.e., (in)equalities).


This paper considers the problem of inference on a class of sets describing a collection of admissible models as solutions to a single smooth inequality.


This paper derives a central limit theorem for the maximum of a sum of high dimensional random vectors.


This paper provides inference methods for best linear approximations to functions which are known to lie within a band.


This paper develops a new direct approach to approximating suprema of general empirical processes by a sequence of suprema of Gaussian processes, without taking the route of approximating empirical processes themselves in the supnorm.


This paper studies identification and estimation in a binary response model with random coefficients B allowed to be correlated with regressors X.The objective is to identifiy the mean of the distribution of B and estimate a trimmed mean of this distribution.


Virtually all methods aimed at correcting for covariate measurement error in regressions rely on some form of additional information (e.g. validation data, known error distributions, repeated measurements or instruments). In contrast, we establish that the fully nonparametric classical errorsinvariables mode is identifiable from data on the regressor and the dependent variable alone, unless the model takes a very specific parametric form.


In this paper we study nonparametric estimation in a binary treatment model where the outcome equation is of unrestricted form, and the selection equation contains multiple unobservables that enter through a nonparametric random coefficients specification.


We compare earnings inequality and mobility across the U.S., Canada, France, Germany and the U.K.
during the late 1990s.


This paper considers nonparametric identifiation of a twostage entry and bidding model for auctions which we call the AffiliatedSignal (AS) Model.


This paper considers identification and estimation in models imposing conditional independence restrictions and featuring a scalar disturbance.


In parametric models a sufficient condition for local idenfication is that the vector of moment is differentiable at the true parameter with full rank derivative matrix. This paper shows that additional conditions are often needed in nonlinear, nonparametric models to avoid nonlinearities overwhelming linear effects.


This paper develops a general nonparametric framework for testing monotonicity of a regression function.


In this paper, the author constructs a new test of conditional moment inequalities based on studentised kernel estimates of moment functions.


We propose an alternative (“dual regression”) to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions.


In this paper the authors study a random coefficient model for a binary outcome.


This paper provides a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set.


This paper presents estimates of key preference parameters of the Epstein and Zin (1989, 1991) and Weil (1989) (EZW) recursive utility model, evaluates the models ability to
to fit asset return data relative to other asset pricing models, and investigates the implications of such estimates for the unobservable aggregate wealth return.


This paper characterises the semiparametric efficiency bound for a class of semiparametric models in which the unknown nuisance functions are identifi
ed via nonparametric conditional moment restrictions with possibly nonnested or overlapping conditioning sets, and the
finite dimensional parameters are potentially overidenti
fied via unconditional moment restrictions involving the nuisance functions.


The primary concern of this article is the provision of definitions and tests for exogeneity appropriate for models defined through sets of conditional moment restrictions.


This article reviews the recent literature on the econometric analysis of games where multiple solutions are possible.


This paper proposes efficient estimators of risk measures in a semiparametric GARCH model defined through moment constraints.


We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of √ n− consistent estimators whose cardinality increases with sample size.


This paper proposes a new statistical test of the stochastic dominance efficiency of a given portfolio over a class of portfolios.


We consider approximating a multivariate regression function by an affine combination of onedimensional conditional component regression functions.


This paper proposes an alternative way to test the leverage hypothesis, using realised volatility as an alternative direct nonparametric measure.


This paper was presented at University of California at Berkeley on 15 November 2012


This paper studies simultaneous equations models for two or more discrete outcomes.


This paper was presented at Stanford on 14 November 2012


This paper was presented at LSE on 9 November 2012


This paper was presented on 5 March 2013 at Hebrew University of Jerusalem.


A Cemmap working paper.


This paper develops asymptotic theory for estimated parameters in differentiated product demand systems with a fixed number of products, as the number of markets T increases, taking into account that the market shares are approximated by Monte Carlo integration.


This paper proposes a class of originsmooth approximators of indicators underlying the sumofnegativepart statistic for testing multiple inequalities.


This paper consideres the effect of the enforcement of labour regulations on informality.


The goal of this paper is to develop formal tests to evaluate the relative insample performance of two competing, misspeci
ed nonnested models in the presence of possible data instability


We analyze new data from a randomized control trial conducted in Eritrea.


This paper studies single equation instrumental variable models of ordered choice in which explanatory variables may be endogenous.


We model individual demand for housing over the lifecycle, and show the aggregate implications of this behaviour.


UCL seminar Presentation


We propose a method for modifying a given density forecast in a way that incorporates the information contained in theorybased moment conditions.


This paper examines the restrictions of revealed preference methods using an axiomatic characterization of a measure of predictive success..


We develop a general framework for analyzing the usefulness of imposing parameter restrictions on a forecasting model.


Structural Economic Models, conference presentation, MIT USA


We develop a nonparametric empirical method for deriving welfare rankings for a social planner based on data envelopment, which avoids the need to specify a weighting scheme.


Competition Commission working paper


Social experiments are powerful sources of information about the effectiveness of interventions.


We propose a new method of testing stochastic dominance which improves on existing tests based on bootstrap or subsampling.


In this paper we modify CUE to solve the no moments/large dispersion problem.


This paper develops a concrete formula for the asymptotic distribution of twostep, possibly nonsmooth semiparametric Mestimators under general misspecification.


This paper provides estimators for moments and quantiles of the unknown distribution in this problem.


We estimate the effects of active labour market policies (ALMP) on subsequent employment by nonparametric instrumental variables and matching estimators.


We analyze equilibria in hedonic economies and study conditions that lead to identification of structural preference parameters in hedonic economies with both additive and nonadditive marginal utility and marginal product functions.


This paper gives identification and estimation results for marginal effects in nonlinear panel models.


The most common approach to estimating conditional quantile curves is to fit a curve, typically linear, pointwise for each quantile.


Using many moment conditions can improve efficiency but makes the usual GMM inferences inaccurate.


Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing non stochastic explanatory variables and innovations suspected to be nonnormal.


We consider the identification of a Markov process {W<sub>t</sub>, X<sub>t</sub>*} for t=1,2,...,T when only {W<sub>t</sub>} for t=1, 2,..,T is observed.


We develop inference tools in a semiparametric partially linear regression model with missing response data.


This paper proposes a new class of HAC covariance matrix estimators.


This paper considers efficient estimation of copulabased semiparametric strictly stationary Markov models.


This Mathematica notebook accompanies the paper, 'Sharp identified sets for discrete variable IV models'.


This paper considers parametric estimation problems with i.i.d. data.


In this paper,we construct a nonparametric estimator of the distributions of latent factors in linear independent multifactor models under the assumption that factor loadings are known.


The paper extends the analysis of structural quantile functions with endogenous arguments to cases in which there are discrete outcomes.


We show that the 2SLS biases relative to that of the OLS biases are then similar for the equations in differences and levels, as are the size distortions of the Wald tests.


Hedonic pricing with quasilinear preferences is shown to be equivalent to stable matching with transferable utilities and a participation constraint, and to an optimal transportation (MongeKantorovich) linear programming problem.


This paper applies a regularization procedure called increasing rearrangement to monotonize Edgeworth and CornishFisher expansions and any other related approximations of distribution and quantile functions of sample statistics.


This paper provides a control function estimator to adjust for endogeneity in the triangular simultaneous equations model where there are no available exclusion restrictions to generate suitable instruments.


This paper evaluates the effect of excise taxes and bans on smoking in public places on the exposure to tobacco smoke of nonsmokers.


This paper explores efficiency gains which might be achievable using moment conditions which are nonlinear in the disturbances and are based on flexible parametric families for error distributions.


This paper develops methods for evaluating marginal policy changes.


University of East Anglia Economics Seminar


seminar, competition commision


This paper is concerned with inference about a function <i>g</i> that is identified by a conditional quantile restriction involving instrumental variables.


We show that these estimates can always be improved with no harm using rearrangement techniques


This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (Θ) and unknown functions (h)of endogenous variables.


We study a mixed hittingtime (MHT) model that specifies durations as the first time a Levy process  a continuoustime process with stationary and independent increments  crosses a heterogeneous threshold.


In this paper we examine the implications of the statistical large sample theory for the computational complexity of Bayesian and quasiBayesian estimation carried out using Metropolis random walks.


For a simplified structural equation/IV regression model with one rightside endogenous variable, we obtain the exact conditional distribution function for Moreira's (2003) conditional likelihood ratio (CLR) test


The behaviour of the permanent and transitory economic shocks for different levels of households' welfare is studied using both consumption and income measures.


This paper extends Imbens and Manski's (2004) analysis of confidence intervals for interval identified parameters.


This paper considers identification and estimation of a nonparametric regression model with an unobserved discrete covariate.


WalrasBowley lecture at the North American summer meeting of the Econometric Society


Econometrics seminar, cemmap


We show in this paper that an iterative conditioning argument used by Hillier (2006) and Andrews, Moreira, and Stock (2007) to evaluate the cdf in the case <i>m</i> = 1 can be generalized to the case of arbitrary


We study linear factor models under the assumptions that factors are mutually independent and independent of errors, and errors can be correlated to some extent.


This paper extends the method of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to the estimation of not only means, but also distributions of potential outcomes.


By exploiting the richness of the data we use a nonparametric approach without imposing critical parametric model assumptions.


This paper investigates how enforcement of labor regulation affects the firm's use of informal labor and firm performance.


We provide a characterization of the class of weights (or priors) that produce estimators that are firstorder unbiased.


In this paper we present new, very much more efficient, algorithms for computing both the toporder zonal and invariant polynomials.


This paper proposes a formal model selection test for choosing between two competing structural econometric models.


Data is reanalyzed from an important series of 19th century experiments conducted by C. S. Peirce and designed to study the plausibility of the Gaussian law of errors for astronomical observations.


cemmap workshop


Estimation of local, quantilespecific copulabased time series models offers some salient advantages over classical global parametric approaches.


In this paper we estimate the rate of return to firm investments in human capital in the form of formal job training.


presented at Far East and South Asia Meeting of the Econometric Society


CESifo Conference


Workshop on econometrics of demand


Workshop on econometrics of demand


presented at the Far Eastern and South Asian meeting of the Econometric Society, University of Kyoto


This paper presents a new estimator for the mixed proportional hazard model that allows for a nonparametric baseline hazard and timevarying regressors.


We propose a twostage procurement auction model with endogenous entry and uncertain number of actual bidders.


In this paper, we document whether and how much the equalizing force of earnings mobility has changed in France in the 1990s.


We consider crosssectional data that exhibit no spatial correlation, but are feared to be spatially dependent.


We provide a general class of tests for correlation in time series, spatial, spatiotemporal and crosssectional data.


This paper uses revealed preference inequalities to provide tight nonparametric bounds on consumer responses to price changes.


We give corrected standard errors, an extension of Bekker (1994) to nonnormal disturbances, that adjust for many instruments.


This paper presents a method for estimating a class of panel data duration models


This paper proposes a new way to construct confidence sets for a parameter of interest in models comprised of finitely many moment inequalities.


We consider the identification of the average treatment effect in models with continuous endogenous variables whose impact is heterogeneous.


This note considers nonparametric identification of a general nonlinear regression model with a dichotomous regressor subject to misclassification error.


We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV).


Dealing with endogenous regressors is a central challenge of applied research.


We study identification in static, simultaneous move finite games of complete information, where the presence of multiple Nash equilibria may lead to partial identification of the model parameters.


We consider the identification of a Markov process {W<sub>t</sub>, X<sub>t</sub>*} for t=1,2,...,T when only {W<sub>t</sub>} for t=1, 2,..,T is observed.


This paper gives an account of the recent literature on estimating models for panel count data.


In this paper we show that the sharp RDD straightforwardly generalizes to the instances in which the eligibility for the program is established with respect to an observable preprogram measure with eligible individuals selfselecting into the treatment group according to an unknown process.


This paper presents the econometric approach to causal modeling


In this paper, two methodological advancements are developed: First, this methodology permits to combine a data set on previously treated individuals with a data set on new clients when the regressors available in these two data sets do not coincide. It thereby incorporates additional regressors on previously treated that are not available for the current clients. Second, statistical inference on the recommended treatment choice is analyzed and conveyed to the agent, physician or case worker in a comprehensible and transparent way.


We introduce test statistics based on generalized empirical likelihood methods that can be used to test simple hypotheses involving the unknown parameter vector in moment condition time series models.


This chapter studies the microeconometric treatmenteffect and structural approaches to dynamic policy evaluation.


This paper develops IV estimators for unconditional quantile treatment effects (QTE) when the treatment selection is endogenous.


In this paper we consider consumer benefits from increased competition in a differentiated product setting: the spread of nontraditional retail outlets.


I study inverse probability weighted Mestimation under a general missing data scheme.


This paper considers a linear triangular simultaneous equations model with conditional quantile restrictions.


This paper concerns the identification and estimation of a shapeinvariant Engel curve system with endogenous total expenditure.


Current methods of estimating the random coefficients logit model employ simulations of the distribution of the taste parameters through pseudorandom sequences.


This paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables.


This paper presents results from a Monte Carlo study concerning inference with spatially dependent data.


A cemmap working paper.


Initially this discussion briefly reviews the contributions of Andrews and Stock and Kitamura, henceforth A, S and K respectively. Because the breadth of material covered by AS and K is so vast, we concentrate only on a few topics.


A cemmap working paper.


A cemmap working paper.


A cemmap working paper.


A cemmap working paper.


We provide simulation evidence on the small sample performance of our estimator, and we apply our method to a Chinese production dataset.


This paper studies the identification of partial differences of nonseparable structural functions.


We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression "error" conditional on an instrumental variable to be zero.


This paper develops a class of first order equivalent semiparametric efficient estimators and tests for conditional moment restrictions models based on a local or kernelweighted version of the CressieRead power divergence family of discrepancies. This approach is similar in spirit to the empirical likelihood methods of Kitamura, Tripathi and Ahn (2004) and Tripathi and Kitamura (2003).


The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels.


This paper presents new identification results for the class of structural dynamic optimal stopping time models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust.


This paper studies models for discrete outcomes which permit explanatory variables to be endogenous.


In this paper I develop a new approach for identification and estimation of the parameters of an oligopoly model, without relying on a potentially unverifiable equilibrium assumption.


This paper is concerned with inference about a function <i>g</i> that is identified by a conditional quantile restriction involving instrumental variables.


This paper introduces biascorrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity.


We provide a general class of tests for correlation in time series, spatial, spatiotemporal and crosssectional data.


For the problem of testing the hypothesis that all m coefficients of the RHS endogenous variables in an IV regression are zero, the likelihood ratio (LR) test can, if the reduced form covariance matrix is known, be rendered similar by a conditioning argument.


This paper proposes a new way to construct confidence sets for a parameter of interest in models comprised of finitely many moment inequalities.


This paper is concerned with identification of a competing risks model with unknown transformations of latent failure times.


Choosing among a number of available treatments the most suitable for a given subject is an issue of everyday concern. A physician has to choose an appropriate drug treatment or medical treatment for a given patient, based on a number of observed covariates <i> X </i>?and prior experience.


For a simplified structural equation/IV regression model with one rightside endogenous variable, we obtain the exact conditional distribution function for Moreira's (2003) conditional likelihood ratio (CLR) test.


The behaviour of the permanent and transitory economic shocks for differen levels of hoouseholds' welfare is studied using both consumption and income measures.


This paper gives an account of the recent literature on estimating models for panel count data.


This paper is a revised version of CWP01/06


Two classes of quantile regression estimation methods for the recursive structural equation models of Chesher (2003) are investigated.


A hedonic price function describes the equilibrium relationship between characteristics of a product and its price.


This paper proposes a formal model selection test for choosing between two competing structural econometric models.


This paper formalizes conditions under which a population distribution ofcategorical responses to attitudinal questions (items) has a scale representation; developstests for whether a particular sample of item responses is consistent with a scale representation; develops methods for nonparametrically estimating the relation between an outcome and a scale value; and generalizes the foregoing to the multiscale case.


We estimate the effects of active labour market policies (ALMP) on subsequent employment by nonparametric instrumental variables and matching estimators.


We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV).


We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression "error" conditional on an instrumental variable to be zero.


The use of fractional imputation, nearest neighbour imputation, predictive mean matching and propensity score weighting are considered. Properties of point estimators are compared both theoretically and by simulation.


Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing non stochastic explanatory variables and innovations suspected to be nonnormal.


We consider the estimation of parametric models for stationary spatial or spatiotemporal data on a ddimensional lattice, for d ? 2.


In this paper we study the impact of misreported treatment status on the estimation of causal treatment effects.


In this paper we study the impact of misreported treatment status on the estimation of causal treatment effects.


In this paper we consider consumer benefits from increased competition in a differentiated product setting: the spread of nontraditional retail outlets.


This paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables.


In this note we consider several versions of the bootstrap and argue that it is helpful in explaining and thinking about such procedures to use an explicit representation of the random resampling process.


Recent work by Schennach has opened the way to a Bayesian treatment of quantile regression.


This paper investigates how enforcement of labor regulation affects the firm's use of informal labor and firm performance.


Current methods of estimating the random coefficients logit model employ simulations of the distribution of the taste parameters through pseudorandom sequences.


The paper provides significant simplifications and extensions of results obtained by Gorsich, Genton, and Strang (J. Multivariate Anal.


The paper provides significant simplifications and extensions of results obtained by Gorsich, Genton, and Strang (J. Multivariate Anal. 80 (2002) 138) on the structure of spatial design matrices.


In this paper we estimate the rate of return to firm investments in human capital in the form of formal job training.


This article is concerned with estimating the additive components of a nonparametric additive quantile regression model.


The purpose of this report is to examine the consistency and reliability of the activityhistory data collected in the FACS. Using data from the first five waves of the FACSand from the first thirteen waves of the British Household Panel Survey (BHPS) as acomparison survey, carefully matched samples have been analysed to calibrate thecompleteness and consistency of the activity history data collected in the FACS andto test whether the FACS generates labour market statistics similar to the comparisonsurvey.


An outcome is determined by a structural function in which theeect of variables of interest is transmitted through a scalar function of those variables  an index. Multiple sources of stochastic variation are permitted to appear asarguments of the structural function, but not as arguments of the index. Conditionsare provided under which there is local identi?cation of ratios of partial derivativesof the index.


Using many moment conditions can improve efficiency but makes the usual GMM inferences inaccurate.


This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model.


This paper studies the estimation of conditional quantiles of counts.


We suggest two nonparametric approaches, based on kernel methods and orthogonal series, respectively, to estimating regression functions in the presence of instrumental variables.


This paper is concerned with identification of a competing risks model with unknown transformations of latent failure times.


The principal purpose of this paper is to adapt to the conditional moment context the GEL unconditional moment methods described in Smith(1997, 2001) and Newey and Smith(2004).


Initially this discussion briefly reviews the contributions of Andrews and Stock and Kitamura.


This paper develops a class of first order equivalent semiparametric efficient estimators and tests for conditional moment restrictions models based on a local or kernelweighted version of the CressieRead power divergence family of discrepancies.


This paper uses revealed preference inequalities to provide tight nonparametric bounds on consumer responses to price changes.


This paper uses revealed preference inequalities to provide tight nonparametric bounds on consumer responses to price changes.


We investigate the performance of a class of semiparametric estimators of the treatment effect via asymptotic expansions.


This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters


The principal purpose of this paper is to describe the performance of generalized empirical likelihood (GEL) methods for time series instrumental variable models specified by nonlinear moment restrictions when identification may be weak.


This paper considers structural nonparametric random utility models for continuous choice variables.


This paper provides a control function estimator to adjust for endogeneity in the triangular simultaneous equations model where there


We consider the number of unit root tests for micro panels where the number of individuals is typically large, but the number of time periods is often very small.


This paper considers parametric estimation problems with i.i.d. data.


We develop a simulated ML method for shortpanel estimation of one or more dynamic linear equations, where the dependent variables are only partially observed through ordinal scales.


I show that a class of fixed effects estimators is reasonably robust for estimating the populationaveraged slope coefficients in panel data models with individualspecific slopes, where the slopes are allowed to be correlated with the covariates.


Approaches to biasreduction are discussed


The analysis in this paper is motivated by the controversial empirical findings and by recent developments in econometrics for partial identification.


The existence of a uniformly consistent estimator for a particular parameter is wellknown to depend on the uniform continuity of the functional that defines the parameter in terms of the model.


This paper investigates the relative significance of differences in cognitive skills and discrimination in explaining racial/ethnic wage gaps.


This paper looks at the policy debate surrounding private pensions and retirement patterns in the UK.


We analyze equilibria in hedonic economies and study conditions that lead to identification of structural preference parameters in hedonic economies with both additive and nonadditive marginal utility and marginal product functions.


This paper proposes to answer this question by using a unique dataset from Norway.


This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete.


This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a standard HAC/bootstrap theory applies, but at the expense of throwing away data and perhaps losing effciency. An alternative is to use some sort of imputation method, but this requires additional modelling assumptions, which we would rather avoid.1 We show how the sampling theory changes and how to modify the resampling algorithms to accommodate the problem of missing data.


I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models.


This paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables.


GEL methods which generalize and extend previous contributions are defined and analysed for moment condition models specified in terms of weakly dependent data.


This paper describes an estimator of the additive components of a nonparametric additive model with a known link function.


We show that these two types of interval estimate are different in practice, the latter in general being shorter.


This paper considers a linear triangular simultaneous equations model with conditional quantile restrictions.


In a number of semiparametric models, smoothing seems necessary in order to obtain estimates of the parametric component which are asymptotically normal and converge at parametric rate.


In additive error models with a discrete endogenous variable identification cannot be achieved under a marginal covariation condition when the support of instruments is sparse relative to the support of the endogenous variable


It is shown that a general form of pessimistic portfolio optimization based on the Choquet approach may be formulated as a problem of linear quantile regression.


This lecture explores conditions under which there is identification of the impact on an outcome of exogenous variation in a variable which is endogenous when data are gathered.


This paper develops and implements semiparametric methods for estimating binary response (binary choice) models withcontinuous endogenous regressors.


Fixed effects estimators of panel models can be severely biased because of the wellknown incidental parameters problem.


An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments.


Recent developments in the theory of choice under uncertainty and risk yield a pessimistic decision theory that replaces the classical expected utility criterion with a Choquet expectation that accentuates the likelihood of the least favorable outcomes.


This paper extends the nonparametric methods developed by Samuelson (1948), Houthakker (1950), Afriat (1973), Diewert (1973) and Varian (1982, 1983) to latently separable models.


I show that a class of fixed effects estimators is reasonably robust for estimating the populationaveraged slope coefficients in panel data models with individualspecific slopes, where the slopes are allowed to be correlated with the covariates.


Estimation of heteroskedasticity and autocorrelation consistent covariance matrices (HACs) is a well established problem in time series.


I study inverse probability weighted Mestimation under a general missing data scheme.


This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model.


This paper considers a panel duration model that has a proportional hazards specification with fixed effects.


I show how to identify and estimate the average partial effect of explanatory variables in a model where unobserved heterogeneity interacts with the explanatory variables and may be unconditionally correlated with the explanatory variables.


Two classes of quantile regression estimation methods for the recursive structural equation models of Chesher (2003) are investigated.


This paper extends the nonparametric methods developed by Samuelson (1948), Houthakker (1950), Afriat (1973), Diewert (1973) and Varian (1982, 1983) to latently separable models.


One of the areas of policy research where randomized field trials have been utilized most intensively is welfare reform.


In an effort to improve the small sample properties of generalized method of moments (GMM) estimators, a number of alternative estimators have been suggested.


This paper considers the identification and estimation of hedonic models.


This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function which depends on a discrete endogenous variable and is nonseparable in a latent variate.


This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function which depends on a discrete endogenous variable and is nonseparable in a latent variate.


This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters.


In this paper I summarize some recent developments in the literature on the econometrics of program evaluation.


We consider the estimation of parametric models for stationary spatial orspatiotemporal data on a ddimensional lattice, for d ≥ 2.


For vectors x and w, let r(x;w) be a function that can be nonparametrically estimated consistently and asymptotically normally.


This paper concerns the identification and estimation of a shapeinvariant Engel curve system with endogenous total expenditure.


Parents with higher education levels have children with higher education levels.


Fixed effects estimators of panel models can be severely biased because of the wellknown incidental parameters problem.


This paper presents a method for estimating a class of panel data duration models, under which an unknown transformation of the duration variable is linearly related to the observed explanatory variables and the unobserved heterogeneity (or frailty) with completely known error distributions.


Conditions are derived under which there is local nonparametric identification of values of structural functions and of their derivatives in potentially nonlinear nonseparable models.


We provide easy to verify suffcient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions and simultaneously depends on some preliminary nonparametric estimators.


The principal purpose of this paper is to describe the performance of generalized


Missing values are endemic in the data sets available to econometricians.


In an effort to improve the small sample properties of generalized method of moments


In the last decade a growing body of research has studied inference on partially identified


Measurement errors in survey data on hourly pay may lead to serious upward bias in low pay


An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments.


This article uses factor models to identify and estimate the distributions of counterfactuals.


This paper considers the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors.


We suggest two nonparametric approaches, based on kernel methods and orthogonal series, respectively, to estimating regression functions in the presence of instrumental variables.


We develop inference tools in a semiparametric partially linear regression model with missing response data.


In this paper we show that the sharp RDD straightforwardly generalizes to the instances in which the eligibility for the program is established with respect to an observable preprogram measure with eligible individuals selfselecting into the treatment group according to an unknown process.


This paper is concerned with estimating a conditional quantile function that is assumed to be partially linear.


We propose a procedure for estimating the critical values of the extended Kolmogorov


In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable.


This paper reviews the record of randomized field trials in welfare reform and assesses the implications of that record for the use of randomization.


This paper explores the identifiability of ratios of derivatives of the index function in a model of a duration process in which the impact of covariates on the hazard function passes through a single index.


We provide easy to verify suffcient conditions for the consistency and asymptotic normality


The approximate effects of measurement error on a variety of measures of inequality and poverty are derived.


Four alternative but related approaches to empirical evaluation of policy interventions are studied.


This paper studies the estimation of conditional quantiles of counts.


new methodology that estimates attitudes semiparametrically and estimates actions nonparametrically, as a function of the resulting attitudinal measures, is used to examine the behavioral effects of cultural and economic preferences in the Presidential elections of 1984 and 1992.


This paper studies the identification of partial differences of nonseparable structural functions.


ExpEnd is a Gauss programme for nonlinear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data.


This paper reviews econometric methods for dynamic panel data models, and presents examples that illustrate the use of these procedures.


I provide an overview of inverse probability weighted (IPW) Mestimators for cross section and twoperiod panel data applications


This paper describes an estimator of the additive components of a nonparametric additive model with a known link function.


We consider the identification of the average treatment effect in models with continuous endogenous variables whose impact is heterogeneous.


This paper discusses some issues related to specification estimation of nonlinear models using panel data.


I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models.


The impact of response measurement error in duration data is investigated using small parameter asymptotic approximations and compared with the effect of hazard function heterogeneity.


The goal of this paper is to use a semiparametric reduced form model to estimate the effects of various tuition subsidies.


We compare the finite sample performance of a range of tests of linear restrictions for linear panel data models estimated using Generalised Method of Moments (GMM).


This paper considers a panel duration model that has a proportional hazards specification with fixed effects.


This paper reviews econometric methods for dynamic panel data models, and presents examples that illustrate the use of these procedures.


Much empirical research in economics and other fields is concerned with estimating the mean of a random variable conditional on one or more explanatory variables (conditional mean function).


Conditions are derived under which there is local nonparametric identification of values of structural functions and of their derivatives in potentially nonlinear nonseparable models.


An overview is presented of some parametric and semiparametric models, estimators, and specification tests that can be used to analyze ordered response variables


This paper develops a model in which a continuum of consumers choose froma continuum of locations indexed by school quality.


I provide an overview of inverse probability weighted (IPW) Mestimators for cross section and twoperiod panel data applications.


Economic models for hedonic markets characterize the pricing of bundles of attributes and the demand and supply of these attributes under different assumptions about market structure,


This paper considers the identification and estimation of hedonic models.


The goal of this paper is to use a semiparametric reduced form model to estimate the effects of various tuition subsidies.


In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable.


A statistical problem that arises in several elds is that of estimating the features of an


Conditions are derived under which there is local nonparametric identification of derivatives of structural equations in nonlinear triangular simultaneous equations systems.


This paper considers the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors.


We investigate the performance of a class of semiparametric estimators of the treatment effect via asymptotic expansions.


An exogenous impact function is defined as the derivative of a structural function with respect to an endogenous variable, other variables, including unobservable variables held fixed.


The impact of covariate measurement error on quantile regression functions is investigated using a small variance approximation


This paper develops and implements semiparametric methods for estimating binary response (binary choice) models withcontinuous endogenous


The approximate effects of measurement error on a variety of measures of inequality and poverty are derived.


This paper summarizes our recent research on evaluating the distributional consequences of social programs.


Smoothed estimates of the complex relationships between age and intakes of energy, fat, calcium and vitamin C are obtained for males and females from British National Food Survey data covering the period 197494.


This note gives a theorem concerning choice of covariate distribution.


Methods of estimation of regression coefficients are proposed when the regression function includes a polynomial in a true regressor which is measured with error.


